Abstract: The disclosed subject matter relates to a method for determining a type and a state of an object of interest, comprising: generating a depth map of a scene by means of a depth sensor, the scene containing the object of interest and an occlusion object lying between the depth sensor and the object of interest; computing three 2D occupancy views, each in a different viewing direction, and three 2D occlusion views; feeding each of said occupancy and occlusion views into a different input of a trained convolutional neural network; receiving both a class and a bounding box of the object of interest from the convolutional neural network; and determining the type of the object of interest and the state of the object of interest. The disclosed subject matter further relates to a system for carrying out said method.

Abstract: A method for simulating, at an instant, the illumination of an indoor scene observed by a camera and being illuminated by outdoor light, the method comprising: a first preliminary phase including: obtaining a reflectance map of the scene elaborating normalized radiosity maps for the contribution of the sky and the ground, a second phase carried out within a first given duration from the instant including: obtaining the position of the sun, obtaining a normalized radiosity map for the contribution of the sun, a third phase, including: acquiring an image of the scene, determining brightness scale parameters for the ground, the sky, and the sun. The disclosure also proposes tracking the camera position.

Abstract: The disclosure relates to a computer implemented method for building a lighting adaptable map of an indoor scene, the scene comprising at least one light source and at least one surface, wherein the lighting adaptable map adapts its appearance based on a given light setting, wherein the light setting is defined by the state of each light source, comprising: obtaining first image information of the scene, where all light sources are turned on, estimating a map of the scene based on said first image information, said map comprising radiance information and light reflecting characteristics of the surfaces in the scene, detecting and segmenting individual light sources in the scene based on the estimated map, estimating the radiance contribution of each light source to the scene based on the estimated map, and building the lighting adaptable map by storing the estimated radiance contributions and combining them for any given light setting.

Abstract: The disclosed subject matter relates to a method for determining a type and a state of an object of interest, comprising: generating a depth map of a scene by means of a depth sensor, the scene containing the object of interest and an occlusion object lying between the depth sensor and the object of interest; computing three 2D occupancy views, each in a different viewing direction, and three 2D occlusion views; feeding each of said occupancy and occlusion views into a different input of a trained convolutional neural network; receiving both a class and a bounding box of the object of interest from the convolutional neural network; and determining the type of the object of interest and the state of the object of interest. The disclosed subject matter further relates to a system for carrying out said method.

Abstract: To carry out satisfactory object recognition in a short time. An object recognition method in accordance with an exemplary aspect of the present invention is an object recognition method for recognizing a target object by using a preliminarily-created object model. The object recognition method generates a range image of an observed scene, detects interest points from the range image, extracts first features, the first features being features of an area containing the interest points, carries out a matching process between the first features and second features, the second features being features of an area in the range image of the object model, calculates a transformation matrix based on a result of the matching process, the transformation matrix being for projecting the second features on a coordinate system of the observed scene, and recognizes the target object with respect to the object model based on the transformation matrix.

Abstract: To carry out satisfactory object recognition in a short time. An object recognition method in accordance with an exemplary aspect of the present invention is an object recognition method for recognizing a target object by using a preliminarily-created object model. The object recognition method generates a range image of an observed scene, detects interest points from the range image, extracts first features, the first features being features of an area containing the interest points, carries out a matching process between the first features and second features, the second features being features of an area in the range image of the object model, calculates a transformation matrix based on a result of the matching process, the transformation matrix being for projecting the second features on a coordinate system of the observed scene, and recognizes the target object with respect to the object model based on the transformation matrix.